[doc] Compile CUDA with LLVM
Summary: This patch adds documentation on compiling CUDA with LLVM as requested by many engineers and researchers. It includes not only user guides but also some internals (mostly optimizations) so that early adopters can start hacking and contributing. Quite a few researchers who contacted us haven't used LLVM before, which is unsurprising as it hasn't been long since LLVM picked up CUDA. So I added a short summary to help these folks get started with LLVM. I expect this document to evolve substantially down the road. The user guides will be much simplified after the Clang integration is done. However, the internals should continue growing to include for example performance debugging and key areas to improve. Reviewers: chandlerc, meheff, broune, tra Subscribers: silvas, jingyue, llvm-commits, eliben Differential Revision: http://reviews.llvm.org/D14370 llvm-svn: 252660
This commit is contained in:
Jingyue Wu
parent
489cfe1401
commit
4f2a6cb248
|
|
@ -0,0 +1,192 @@
|
|||
===================================
|
||||
Compiling CUDA C/C++ with LLVM
|
||||
===================================
|
||||
|
||||
.. contents::
|
||||
:local:
|
||||
|
||||
Introduction
|
||||
============
|
||||
|
||||
This document contains the user guides and the internals of compiling CUDA
|
||||
C/C++ with LLVM. It is aimed at both users who want to compile CUDA with LLVM
|
||||
and developers who want to improve LLVM for GPUs. This document assumes a basic
|
||||
familiarity with CUDA. Information about CUDA programming can be found in the
|
||||
`CUDA programming guide
|
||||
<http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html>`_.
|
||||
|
||||
How to Build LLVM with CUDA Support
|
||||
===================================
|
||||
|
||||
The support for CUDA is still in progress and temporarily relies on `this patch
|
||||
<http://reviews.llvm.org/D14452>`_. Below is a quick summary of downloading and
|
||||
building LLVM with CUDA support. Consult the `Getting Started
|
||||
<http://llvm.org/docs/GettingStarted.html>`_ page for more details on setting
|
||||
up LLVM.
|
||||
|
||||
#. Checkout LLVM
|
||||
|
||||
.. code-block:: console
|
||||
|
||||
$ cd where-you-want-llvm-to-live
|
||||
$ svn co http://llvm.org/svn/llvm-project/llvm/trunk llvm
|
||||
|
||||
#. Checkout Clang
|
||||
|
||||
.. code-block:: console
|
||||
|
||||
$ cd where-you-want-llvm-to-live
|
||||
$ cd llvm/tools
|
||||
$ svn co http://llvm.org/svn/llvm-project/cfe/trunk clang
|
||||
|
||||
#. Apply the temporary patch for CUDA support.
|
||||
|
||||
If you have installed `Arcanist
|
||||
<http://llvm.org/docs/Phabricator.html#requesting-a-review-via-the-command-line>`_,
|
||||
you can apply this patch using
|
||||
|
||||
.. code-block:: console
|
||||
|
||||
$ cd where-you-want-llvm-to-live
|
||||
$ cd llvm/tools/clang
|
||||
$ arc patch D14452
|
||||
|
||||
Otherwise, go to `its review page <http://reviews.llvm.org/D14452>`_,
|
||||
download the raw diff, and apply it manually using
|
||||
|
||||
.. code-block:: console
|
||||
|
||||
$ cd where-you-want-llvm-to-live
|
||||
$ cd llvm/tools/clang
|
||||
$ patch -p0 < D14452.diff
|
||||
|
||||
#. Configure and build LLVM and Clang
|
||||
|
||||
.. code-block:: console
|
||||
|
||||
$ cd where-you-want-llvm-to-live
|
||||
$ mkdir build
|
||||
$ cd build
|
||||
$ cmake [options] ..
|
||||
$ make
|
||||
|
||||
How to Compile CUDA C/C++ with LLVM
|
||||
===================================
|
||||
|
||||
We assume you have installed the CUDA driver and runtime. Consult the `NVIDIA
|
||||
CUDA installation Guide
|
||||
<https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html>`_ if
|
||||
you have not.
|
||||
|
||||
Suppose you want to compile and run the following CUDA program (``axpy.cu``)
|
||||
which multiplies a ``float`` array by a ``float`` scalar (AXPY).
|
||||
|
||||
.. code-block:: c++
|
||||
|
||||
#include <helper_cuda.h> // for checkCudaErrors
|
||||
|
||||
#include <iostream>
|
||||
|
||||
__global__ void axpy(float a, float* x, float* y) {
|
||||
y[threadIdx.x] = a * x[threadIdx.x];
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[]) {
|
||||
const int kDataLen = 4;
|
||||
|
||||
float a = 2.0f;
|
||||
float host_x[kDataLen] = {1.0f, 2.0f, 3.0f, 4.0f};
|
||||
float host_y[kDataLen];
|
||||
|
||||
// Copy input data to device.
|
||||
float* device_x;
|
||||
float* device_y;
|
||||
checkCudaErrors(cudaMalloc(&device_x, kDataLen * sizeof(float)));
|
||||
checkCudaErrors(cudaMalloc(&device_y, kDataLen * sizeof(float)));
|
||||
checkCudaErrors(cudaMemcpy(device_x, host_x, kDataLen * sizeof(float),
|
||||
cudaMemcpyHostToDevice));
|
||||
|
||||
// Launch the kernel.
|
||||
axpy<<<1, kDataLen>>>(a, device_x, device_y);
|
||||
|
||||
// Copy output data to host.
|
||||
checkCudaErrors(cudaDeviceSynchronize());
|
||||
checkCudaErrors(cudaMemcpy(host_y, device_y, kDataLen * sizeof(float),
|
||||
cudaMemcpyDeviceToHost));
|
||||
|
||||
// Print the results.
|
||||
for (int i = 0; i < kDataLen; ++i) {
|
||||
std::cout << "y[" << i << "] = " << host_y[i] << "\n";
|
||||
}
|
||||
|
||||
checkCudaErrors(cudaDeviceReset());
|
||||
return 0;
|
||||
}
|
||||
|
||||
The command line for compilation is similar to what you would use for C++.
|
||||
|
||||
.. code-block:: console
|
||||
|
||||
$ clang++ -o axpy -I<CUDA install path>/samples/common/inc -L<CUDA install path>/<lib64 or lib> axpy.cu -lcudart_static -lcuda -ldl -lrt -pthread
|
||||
$ ./axpy
|
||||
y[0] = 2
|
||||
y[1] = 4
|
||||
y[2] = 6
|
||||
y[3] = 8
|
||||
|
||||
Note that ``helper_cuda.h`` comes from the CUDA samples, so you need the
|
||||
samples installed for this example. ``<CUDA install path>`` is the root
|
||||
directory where you installed CUDA SDK, typically ``/usr/local/cuda``.
|
||||
|
||||
Optimizations
|
||||
=============
|
||||
|
||||
CPU and GPU have different design philosophies and architectures. For example, a
|
||||
typical CPU has branch prediction, out-of-order execution, and is superscalar,
|
||||
whereas a typical GPU has none of these. Due to such differences, an
|
||||
optimization pipeline well-tuned for CPUs may be not suitable for GPUs.
|
||||
|
||||
LLVM performs several general and CUDA-specific optimizations for GPUs. The
|
||||
list below shows some of the more important optimizations for GPUs. Most of
|
||||
them have been upstreamed to ``lib/Transforms/Scalar`` and
|
||||
``lib/Target/NVPTX``. A few of them have not been upstreamed due to lack of a
|
||||
customizable target-independent optimization pipeline.
|
||||
|
||||
* **Straight-line scalar optimizations**. These optimizations reduce redundancy
|
||||
in straight-line code. Details can be found in the `design document for
|
||||
straight-line scalar optimizations <https://goo.gl/4Rb9As>`_.
|
||||
|
||||
* **Inferring memory spaces**. `This optimization
|
||||
<http://www.llvm.org/docs/doxygen/html/NVPTXFavorNonGenericAddrSpaces_8cpp_source.html>`_
|
||||
infers the memory space of an address so that the backend can emit faster
|
||||
special loads and stores from it. Details can be found in the `design
|
||||
document for memory space inference <https://goo.gl/5wH2Ct>`_.
|
||||
|
||||
* **Aggressive loop unrooling and function inlining**. Loop unrolling and
|
||||
function inlining need to be more aggressive for GPUs than for CPUs because
|
||||
control flow transfer in GPU is more expensive. They also promote other
|
||||
optimizations such as constant propagation and SROA which sometimes speed up
|
||||
code by over 10x. An empirical inline threshold for GPUs is 1100. This
|
||||
configuration has yet to be upstreamed with a target-specific optimization
|
||||
pipeline. LLVM also provides `loop unrolling pragmas
|
||||
<http://clang.llvm.org/docs/AttributeReference.html#pragma-unroll-pragma-nounroll>`_
|
||||
and ``__attribute__((always_inline))`` for programmers to force unrolling and
|
||||
inling.
|
||||
|
||||
* **Aggressive speculative execution**. `This transformation
|
||||
<http://llvm.org/docs/doxygen/html/SpeculativeExecution_8cpp_source.html>`_ is
|
||||
mainly for promoting straight-line scalar optimizations which are most
|
||||
effective on code along dominator paths.
|
||||
|
||||
* **Memory-space alias analysis**. `This alias analysis
|
||||
<http://llvm.org/docs/NVPTXUsage.html>`_ infers that two pointers in different
|
||||
special memory spaces do not alias. It has yet to be integrated to the new
|
||||
alias analysis infrastructure; the new infrastructure does not run
|
||||
target-specific alias analysis.
|
||||
|
||||
* **Bypassing 64-bit divides**. `An existing optimization
|
||||
<http://llvm.org/docs/doxygen/html/BypassSlowDivision_8cpp_source.html>`_
|
||||
enabled in the NVPTX backend. 64-bit integer divides are much slower than
|
||||
32-bit ones on NVIDIA GPUs due to lack of a divide unit. Many of the 64-bit
|
||||
divides in our benchmarks have a divisor and dividend which fit in 32-bits at
|
||||
runtime. This optimization provides a fast path for this common case.
|
||||
|
|
@ -86,6 +86,7 @@ representation.
|
|||
GetElementPtr
|
||||
Frontend/PerformanceTips
|
||||
MCJITDesignAndImplementation
|
||||
CompileCudaWithLLVM
|
||||
|
||||
:doc:`GettingStarted`
|
||||
Discusses how to get up and running quickly with the LLVM infrastructure.
|
||||
|
|
@ -371,6 +372,9 @@ For API clients and LLVM developers.
|
|||
:doc:`FaultMaps`
|
||||
LLVM support for folding control flow into faulting machine instructions.
|
||||
|
||||
:doc:`CompileCudaWithLLVM`
|
||||
LLVM support for CUDA.
|
||||
|
||||
Development Process Documentation
|
||||
=================================
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue